Explainability of Reinforcement Learning (RL) policies remains a challenging research problem, particularly when considering RL in a safety context.
In complex tasks where the reward function is not straightforward and consists of a set of objectives, multiple reinforcement learning (RL) policies that perform task adequately, but employ different strategies can be trained by adjusting the impact of individual objectives on reward function.
This paper introduces Logical Credal Networks, an expressive probabilistic logic that generalizes many prior models that combine logic and probability.
The pipeline optimization problem in machine learning requires simultaneous optimization of pipeline structures and parameter adaptation of their elements.
Optimizing a machine learning (ML) pipeline has been an important topic of AI and ML.
no code implementations • 2 Feb 2019 • Adi Botea, Christian Muise, Shubham Agarwal, Oznur Alkan, Ondrej Bajgar, Elizabeth Daly, Akihiro Kishimoto, Luis Lastras, Radu Marinescu, Josef Ondrej, Pablo Pedemonte, Miroslav Vodolan
Dialogue systems have many applications such as customer support or question answering.